Financial institutions are accustomed to holding on to historical customer data. They view this as a competitive advantage, as well as a security requirement.
Today, however, regulatory and market changes are driving “open banking”, a buzzword that in practical terms usually means “open API”, that is, requiring customer transaction data to be shareable with fintechs, merchants, or customers themselves.
And this is making banks very nervous. But if they play their cards right, banks, lenders, insurance companies and other financial institutions may find open API is a powerful driver of new revenues.
Banks struggle to understand the information they have on their customers, and they need tools to help them make the right decisions and create the right products that their customers want.
Fintech companies, like gini have developed solutions to help commercial banks, credit card companies and other lenders turn data into insight – and insight into action.
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In Asia, some markets adapted to the open-API trend quickly, such as Korea and India, with regulatory support. Now Australia is mandating open APIs, while regulators in Singapore, Malaysia and Hong Kong are pushing banks to open as well but without explicit direction, leaving it to the marketplace to determine the most relevant use cases.
Some fintechs in these more hands-off environments worry that banks will simply drag their feet. But regulation is not always the most important factor in opening data: the United States, for example, has no such regulatory mandate but has a robust open-API environment, because banks discovered that it helped them compete against disruptive tech players.
So what are some uses cases in a market such as Hong Kong, which is possibly the most developed but also the most traditional banking market in Asia?
Fintechs are building capabilities to help banks and lenders make sense of the troves of data they possess but aren’t able to exploit. They do this by enriching data to build a structured set of data. That’s the first step for a bank looking to use machine learning and AI to improve customer experience and lower operational costs.
Banks may have plenty of historical data about their customers until now they have been flying blind in terms of seeing a customer’s entire portfolio or behaviour.
Fintechs acquire transaction data and “clean” it, eliminating errors, and “enrich” it, making it machine-readable. That data is now being turned into analytics and use cases for banks that need to either reduce their operating costs or generate new revenues.
One of the earliest benefits is removing costs from chargebacks. In Hong Kong, over 10 per cent of calls to banks’ call centres involve questions about card transactions at venues that people do not recognize. This is because many merchants operate under confusing holding-company names.
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This may sound simple, but each investigation costs banks on average around US$100, which adds up to tens of millions of dollars, for every one million customers, over a year. However, the clean data includes geolocation, enabling it to map venues to corporate names – and even to identify individual stores among chains, which are often impossible for banks to figure out. Such data insights not only save banks money on investigations but also reduce the number of incoming calls.
The same kind of structured data can also be used to make money, not just save it. One of the biggest demands among banks is to personalize rewards and offers, delivering the right product to the right customer at the right time.
To date, banks have relied on their proprietary demographic information to come up with new product offers, which tend to be one-size-fits-all pitches.
For banks to be able to leverage more data better, the advent of open API is a game-changer.
A financial institution can work through technology companies, such as gini to access the data from, say, an e-commerce site or a merchant, and develop new products for those customers. And as banks become more comfortable with data exchange, they will begin to embrace the greater value from open API collaborations.
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